Document Detail

Variational bayesian blind deconvolution using a total variation prior.
MedLine Citation:
PMID:  19095515     Owner:  NLM     Status:  MEDLINE    
In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.
S Derin Babacan; Rafael Molina; Aggelos K Katsaggelos
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  18     ISSN:  1057-7149     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2009 Jan 
Date Detail:
Created Date:  2008-12-19     Completed Date:  2009-02-19     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  United States    
Other Details:
Languages:  eng     Pagination:  12-26     Citation Subset:  IM    
Department of Electrical Engineering and Computer Science, Northwestern University, IL 60208-3118, USA.
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MeSH Terms
Artificial Intelligence*
Bayes Theorem
Computer Simulation
Image Enhancement / methods*
Image Interpretation, Computer-Assisted / methods*
Models, Statistical
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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